Estimation of Position and Orientation using Wireless LAN Signal Strength and Artificial Neural Networks
Nowadays localisation and tracking of mobile systems in indoor enviroments is an important point at many research institutions. This talk gives an overview over currently used technologies an algorithms in this field of research.
The overview explains how wireless signals can be used in different ways to estimate positions and orientations of mobile systems in indoor enviroments. Furthermore the differences of the accuracies that can be reached and thereby appearing problems are shown. Example applications demonstrate how this technologies can be used.
The wireless LAN localisation is an inexpensive possibility to localise mobile devices and vehicles. For tracking such systems not only the position is important but also the orientation. If the position and orientation is known a global localisation of the movement trajectory can be estimated.
With Artifical Neural Networks (ANNs) non linear functions can be approximated. The signal strengthes of radio signals can be used as inputs and the corresponding positions and orientations as outputs for such networks. The talk shows experimental results of measurents using ANNs for localisation in an office building.
Estimation of Position and Orientation using Wireless LAN Signal Strength and Artificial Neural Networks
Nowadays localisation and tracking of mobile systems in indoor enviroments is an important point at many research institutions. This talk gives an overview over currently used technologies an algorithms in this field of research.
The overview explains how wireless signals can be used in different ways to estimate positions and orientations of mobile systems in indoor enviroments. Furthermore the differences of the accuracies that can be reached and thereby appearing problems are shown. Example applications demonstrate how this technologies can be used.
The wireless LAN localisation is an inexpensive possibility to localise mobile devices and vehicles. For tracking such systems not only the position is important but also the orientation. If the position and orientation is known a global localisation of the movement trajectory can be estimated.
With Artifical Neural Networks (ANNs) non linear functions can be approximated. The signal strengthes of radio signals can be used as inputs and the corresponding positions and orientations as outputs for such networks. The talk shows experimental results of measurents using ANNs for localisation in an office building.
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